@phdthesis{19557,
  author       = {Schwarz, Lena A},
  issn         = {2663-337X},
  pages        = {124},
  publisher    = {Institute of Science and Technology Austria},
  title        = {{Mapping developmental dynamics of autism spectrum disorder mouse models at single-cell resolution}},
  doi          = {10.15479/AT-ISTA-19557},
  year         = {2025},
}

@article{19704,
  abstract     = {The information-processing capability of the brain’s cellular network depends on the physical wiring pattern between neurons and their molecular and functional characteristics. Mapping neurons and resolving their individual synaptic connections can be achieved by volumetric imaging at nanoscale resolution1,2 with dense cellular labelling. Light microscopy is uniquely positioned to visualize specific molecules, but dense, synapse-level circuit reconstruction by light microscopy has been out of reach, owing to limitations in resolution, contrast and volumetric imaging capability. Here we describe light-microscopy-based connectomics (LICONN). We integrated specifically engineered hydrogel embedding and expansion with comprehensive deep-learning-based segmentation and analysis of connectivity, thereby directly incorporating molecular information into synapse-level reconstructions of brain tissue. LICONN will allow synapse-level phenotyping of brain tissue in biological experiments in a readily adoptable manner.},
  author       = {Tavakoli, Mojtaba and Lyudchik, Julia and Januszewski, Michał and Vistunou, Vitali and Agudelo Duenas, Nathalie and Vorlaufer, Jakob and Sommer, Christoph M and Kreuzinger, Caroline and Oliveira, Bárbara and Cenameri, Alban and Novarino, Gaia and Jain, Viren and Danzl, Johann G},
  issn         = {1476-4687},
  journal      = {Nature},
  pages        = {398--410},
  publisher    = {Springer Nature},
  title        = {{Light-microscopy-based connectomic reconstruction of mammalian brain tissue}},
  doi          = {10.1038/s41586-025-08985-1},
  volume       = {642},
  year         = {2025},
}

@unpublished{18677,
  abstract     = {The information-processing capability of the brain’s cellular network depends on the physical wiring pattern between neurons and their molecular and functional characteristics. Mapping neurons and resolving their individual synaptic connections can be achieved by volumetric imaging at nanoscale resolution with dense cellular labeling. Light microscopy is uniquely positioned to visualize specific molecules but dense, synapse-level circuit reconstruction by light microscopy has been out of reach due to limitations in resolution, contrast, and volumetric imaging capability. Here we developed light-microscopy based connectomics (LICONN). We integrated specifically engineered hydrogel embedding and expansion with comprehensive deep-learning based segmentation and analysis of connectivity, thus directly incorporating molecular information in synapse-level brain tissue reconstructions. LICONN will allow synapse-level brain tissue phenotyping in biological experiments in a readily adoptable manner.},
  author       = {Tavakoli, Mojtaba and Lyudchik, Julia and Januszewski, Michał and Vistunou, Vitali and Agudelo Duenas, Nathalie and Vorlaufer, Jakob and Sommer, Christoph M and Kreuzinger, Caroline and Oliveira, Bárbara and Cenameri, Alban and Novarino, Gaia and Jain, Viren and Danzl, Johann G},
  booktitle    = {bioRxiv},
  title        = {{Light-microscopy based dense connectomic reconstruction of mammalian brain tissue}},
  doi          = {10.1101/2024.03.01.582884},
  year         = {2024},
}